Evidence map›Paper›PMID 27896994›Full record

ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2017

HUMAN KINASES DISPLAY MUTATIONAL HOTSPOTS AT COGNATE POSITIONS WITHIN CANCER.

Jonathan Gallion, Angela D Wilkins, Olivier Lichtarge

Open access · goldAbstract read
In one paragraph

Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 83% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Recurrent high-impact mutations at cognate structural positions in class A G protein-coupled receptors expressed in tumors.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Jonathan GallionStructural Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza Houston, TX, 77030, USA†The authors gratefully acknowledge support from the National Institutes of Health (GM066099 and GM079656), from the National Science Foundation (DBI-1356569), and from DARPA (N66001-15-C-4042), Jonathan.Gallion@bcm.edu.
Angela D Wilkins
Olivier Lichtarge
Baylor College of Medicine · US

Funding

Functional Determinnats in G Protein-coupled ReceptorsR01GM066099 · NIGMS · BAYLOR COLLEGE OF MEDICINE · PI LICHTARGE, OLIVIER · 2003 to 2022
$7.5M
Comparative genomics of protein structure and functionR01GM079656 · NIGMS · BAYLOR COLLEGE OF MEDICINE · PI LICHTARGE, OLIVIER · 2007 to 2019
$4.3M
NIGMS NIH HHS R01 GM066099NIGMS NIH HHS R01 GM079656
6 · The paper itself

Abstract

The discovery of driver genes is a major pursuit of cancer genomics, usually based on observing the same mutation in different patients. But the heterogeneity of cancer pathways plus the high background mutational frequency of tumor cells often cloud the distinction between less frequent drivers and innocent passenger mutations. Here, to overcome these disadvantages, we grouped together mutations from close kinase paralogs under the hypothesis that cognate mutations may functionally favor cancer cells in similar ways. Indeed, we find that kinase paralogs often bear mutations to the same substituted amino acid at the same aligned positions and with a large predicted Evolutionary Action. Functionally, these high Evolutionary Action, non-random mutations affect known kinase motifs, but strikingly, they do so differently among different kinase types and cancers, consistent with differences in selective pressures. Taken together, these results suggest that cancer pathways may flexibly distribute a dependence on a given functional mutation among multiple close kinase paralogs. The recognition of this "mutational delocalization" of cancer drivers among groups of paralogs is a new phenomena that may help better identify relevant mechanisms and therefore eventually guide personalized therapy.

Indexed as

MutationComputational BiologyEvolution, MolecularHumansModels, MolecularNeoplasmsPhosphotransferasesProtein ConformationPhosphotransferases

Identifiers

PMID27896994
PMCPMC5157159
OpenAlexW2555141543

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.